This contribution analyzes the self-perception and political biases of OpenAI’s Large Language Model ChatGPT. Considering the first small-scale reports and studies that have emerged, claiming that ChatGPT is politically biased towards progressive and libertarian points of view, this contribution is aimed at providing further clarity on this subject. Although the concept of political bias and affiliation is hard to define, lacking an agreed-upon measure for its quantification, this contribution attempts to examine this issue by having ChatGPT respond to questions on commonly used measures of political bias. In addition, further measures for personality traits that have previously been linked to political affiliations were examined. More specifically, ChatGPT was asked to answer the questions posed by the political compass test as well as similar questionnaires that are specific to the respective politics of the G7 member states. These eight tests were repeated ten times each and indicate that ChatGPT seems to hold a bias towards progressive views. The political compass test revealed a bias towards progressive and libertarian views, supporting the claims of prior research. The political questionnaires for the G7 member states indicated a bias towards progressive views but no significant bias between authoritarian and libertarian views, contradicting the findings of prior reports. In addition, ChatGPT’s Big Five personality traits were tested using the OCEAN test, and its personality type was queried using the Myers-Briggs Type Indicator (MBTI) test. Finally, the maliciousness of ChatGPT was evaluated using the Dark Factor test. These three tests were also repeated ten times each, revealing that ChatGPT perceives itself as highly open and agreeable, has the Myers-Briggs personality type ENFJ, and is among the test-takers with the least pronounced dark traits.

This study examines the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information. Our analysis is based upon comparing party responses to 38 statements of the Wahl-O-Mat, a well-established German online tool that helps inform voters by comparing their views with political party positions. For the LLMs, we identify significant biases. They exhibit a strong alignment (over 75% on average) with left-wing parties and a substantially lower alignment with center-right (smaller 50%) and right-wing parties (around 30%). Furthermore, for the VAAs, intended to objectively inform voters, we found substantial deviations from the parties’ stated positions in Wahl-O-Mat: While one VAA deviated in 25% of cases, another VAA showed deviations in more than 50% of cases. For the latter, we even observed that simple prompt injections led to severe hallucinations, including false claims such as non-existent connections between political parties and right-wing extremist ties.

In extant literature the terms automated guided vehicle (AGV), autonomous mobile robot (AMR), and mobile robot (MR) are used with different interpretations. Since the terms are closely related to technical characteristics, features, and possible applications, inconsistent use of these terms hinders their interoperability, and complicates the integration of systems across the warehousing domain. This paper defines the terms AGV, AMR, MR, based on the definitions from a systematic literature review. The study relies on English and German-language research from 2000 until 2025. Risk of bias in inclusion of studies was assessed using predefined evidence grades and content relevance grades. In total, 177 publications were included in the final analysis, clustered by locus of control, freedom of manipulation, collaborative ability, and software capability. The key findings show that the definitions of AGVs overlap with new AMR and MR concepts. The decisive factor is the ability of the vehicle or system to be autonomous. Building on the new definitions, we introduce a spectrum of autonomy maturity levels, ranging from remotely-controlled (zero autonomy) to fully autonomous (independent) systems. Understanding the nature and appropriate autonomy level of a system is essential to support scalability, reduce information asymmetries among vendors and users, and accelerate innovation in the rapidly evolving field of mobile robotics. We also identify implications for future research based on our findings in the concluding section.

The performance of Large Language Models, such as ChatGPT, generally increases with each further model release. In this paper, we investigate whether and how well different ChatGPT models solve the exams of three different logistics undergraduate courses. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both, the free version (GPT-4o mini) and the chargeable version (GPT-4o) completed three different logistics exams using three different prompting techniques (with and without role assignment as logistics expert or student). The o1-preview model was also used (without role assignment) for six attempts. The tests were repeated three times. A total of 60 tests were completed and compared to in-class results of logistics students. The results show that a total of 46 tests were passed. The best attempt solved 93% of an exam correctly. Compared to the students from the respective semester, ChatGPT outperforms students in one exam. On the other two exams the students perform better on average.

Background: Serious games and game-based learning are widely used in education. Gaming in logistics education relies on serious gaming with no or less consideration on enjoyment. Intervention: This study examines in a first step whether and if so, how (serious) games are used as part of logistics majors’ curricula at German universities. Based on the findings, an entertainment game about warehousing is developed and validated. Methods: Warehousing is an application-based discipline in which the operation of different picking techniques has an impact on the order picking. To help students understand the impact of such techniques, the 2D game WareMover was developed. The open-access game combines education and entertainment with different gamification objects and can be played in singleplayer or multiplayer mode. In this competitive 2D game, the objective is to pick orders by navigating around the warehouse towards the right shelf and to click on the correct storage compartment. The player has to achieve the highest score, which is determined by the fastest and least erroneous delivery. Four different picking techniques can be used in two game modes. Results: The results of a 14-player game session are presented. All players played both game variants with all picking techniques and completed a questionnaire. The players rated the game as user-friendly, entertaining, didactically useful, suitable for teaching and education, and engaging. In addition, the majority of players would play the game again and recommend it to others. Conclusion: The open-source game WareMover can contribute to studies in the field of warehousing by teaching students about picking techniques and the differences between these techniques. The novel game approach of interpreting a serious game as an entertainment game can increase the enjoyment and motivation of the players.

The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both the free version (GPT-4o mini) and the premium version (GPT-4o) completed three different warehousing exams using three different prompting techniques (with and without role assignments as logistics experts or students). The o1-preview model was also used (without a role assignment) for six runs. The tests were repeated three times. A total of 60 tests were conducted and compared with the in-class results of logistics students. The results show that the GPT models passed a total of 46 tests. The best run solved 93% of the exam correctly. Compared with the students from the respective semester, ChatGPT outperformed the students in one exam. In the other two exams, the students performed better on average than ChatGPT.